30-Day-Ahead Load Forecasting for the Rajshahi Zone of the Bangladesh Power System: A Linear-Base Boosted BiLSTM Approach

This paper develops a 30-day-ahead daily peak load forecasting framework for the Rajshahi zone of the Bangladesh Power System, a horizon that governs maintenance scheduling, fuel procurement and outage planning but has received far less attention than day-ahead forecasting, and almost none for supply-constrained tropical grids.

Three contributions are made. First, a regularised linear stage is fitted before gradient boosting, so that the 4.2% per year growth trend is carried by a learner that can extrapolate it; this removes an extrapolation bias worth 0.69 percentage points against plain XGBoost on identical features. Second, supply-side outages are screened by a local V-drop criterion so that load shedding is not learned as demand, and no reported score is computed against an imputed target. Third, the worth of the target-date weather forecast is measured rather than assumed, at 2.80 percentage points, which is larger than the spread across the twenty-five models compared and identifies forecast-weather quality rather than model choice as the dominant lever at this horizon.

Evaluation is on a strictly chronological 2025 hold-out year against twenty-four benchmarks, with Diebold-Mariano tests, four-fold rolling-origin validation and seed-averaged deep models.